(1) Field of Invention
The present invention relates to a system for supporting human intelligence analysis and, more particularly, to a system for supporting human intelligence analysis by enabling high fidelity understanding of a local and regional social situation.
(2) Description of Related Art
HUMINT, an abbreviation of the words human intelligence, refers to intelligence gathering by means of interpersonal contact. In other words, intelligence is derived from information collected and provided by human sources. HUMINT activities typically consist of interrogations and conversations with persons having access to pertinent information. Good intelligence management begins with the proper determination of what is considered useful information. Data collected through HUMINT activities must be evaluated and transformed into a usable form and often stored for flame use.
HUMINT collection and analysis is currently a laborious and manual process prone to information loss and missed opportunities. Providing automated support to users (e.g., military personnel) requires addressing many operational and technical challenges. Relevant research addressing uncertain and incomplete network data is primarily focused on sampling issues of node attributes for classification. Several references (see List of Cited Literature References, Literature Reference Nos. 4 and 14) deal with optimal sampling that takes graph structure into account, but they only handle the issue of sampling nodes for labeling. Entity resolution approaches (see Literature Reference Nos. 2 and 16) deal with ambiguous names, but do not deal with purposely misleading information.
Works on link analysis (see Literature Reference No. 5) can be explored to extract relevant structure; however, both are limited to homogenous networks. Additional works (see Literature Reference No. 10) applied similarity extraction before spectral methods (see Literature Reference No. 12) to handle heterogeneous networks, but were slow at handling the heterogeneous networks. Existing work on opinion summarization generated structured summaries (see Literature Reference Nos. 7, 8, and 20) for products based on customer review, but they are limited to specific topics.
Furthermore, mutual influence of techno-social systems has been overviewed (see Literature Reference No. 15), and dynamic network agent-based models were studied for opinion and community co-evolution (see Literature Reference No. 9). However, these studies were limited to theoretical modeling and did not include learning from empirical network data. Works in on-line social media have modeled social influence in connecting people to their friends, followers, and collaborators (see Literature Reference No. 13), but were limited to positive links. Another reference (see Literature Reference No. 11) proposed an edge sign prediction problem to model social influence for trusts with positive and negative links, but it was limited to static links with no neutral links.
Each of the prior methods discussed above exhibit limitations that make them incomplete. The methods have significant limitations in (1) handling uncertain, missing, and conflicting data, (2) handling untrustworthy or deceptive data (3) providing relevant information and contexts to validate and acquire new information, and (4) tracing information trends to assist in knowledge acquisition from prioritized targets. The invention described herein addresses the shortcomings in current practices of HUMINT data collection and analysis by going beyond traditional topological approaches and, instead, emphasizing, the network of modeling, of trustworthy relation analysis, mission-based social understanding, and adaptability to dynamic situations.